A common assumption in classical control systems for state estimation is that samples are collected periodically from some prescribed output sensors [1]}, [2]}, [3]}. In practice, sampling strategies are designed subject to some given performance criteria and hardware/software constraints, e.g., achieving certain estimation quality, data processing power, battery-life of the sensors and processors, etc. Although over-sampling may result in superior estimation quality, it is usually undesirable in networked systems that are equipped with spatially distributed sensors; examples include, spatially distributed networked robots, synchronous power networks, and platoon of self-driving vehicles. In these applications, designing sparse sampling strategies, that allow collecting samples aperiodically from only a fraction of subsystems, will reduce sensing costs due to the existing algorithmic, physical, hardware, and software constraints. These burdens are even more pronounced in networks with several thousands subsystems. Our goal in this paper is to propose a formal method to study properties and performance of various sampling strategies and devise scalable algorithms to design sparse sampling strategies in space and time with provable performance bounds.
